CN108491922A - Active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm - Google Patents
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Abstract
The invention discloses the active distribution network Intelligent Hybrid reconstructing methods based on teaching and particle cluster algorithm, using loss minimization as Optimization goal.Including：Random initializtion population；Calculate the fitness function of each particle；The position and speed of each particle is updated using particle cluster algorithm；Teaching algorithm is recycled to be updated the position of each particle；It seeks the fitness function of each population and excludes infeasible solution；The optimal solution and globally optimal solution for updating population, until reaching maximum iteration.This method needle is in connection by teaching algorithm and particle cluster algorithm, while retaining particle cluster algorithm global optimizing ability, the local optimal searching ability and speed of algorithm is enhanced, to realize algorithm in global and local optimizing ability.The present invention updates network structure by optimization algorithm iteration, plays the role of reducing network loss.
Description
Technical field
The present invention relates to the active distribution network Intelligent Hybrid reconstructing methods based on teaching and particle cluster algorithm.
Background technology
Power distribution network reconfiguration is exactly the assembled state by changing block switch, interconnection switch, to change network topology structure
With the supply path of user.Current derivation algorithm can be divided into traditional mathematics algorithm, heuritic approach and intelligent algorithm
Deng.Wherein, the calculating time of mathematical algorithm is long；Heuritic approach is influenced to compare by the original state and network size of network
Greatly, differ and surely obtain optimal solution；Intelligent algorithm is not influenced by network initial state, but the convergence of some algorithms
Speed is slow, is easily trapped into local optimum, to influence the efficiency and accuracy of power distribution network reconfiguration.
Particle cluster algorithm (Particle Swarm Optimization, PSO) and teaching algorithm (Teaching
Learning Based optimization, TLBO) it is all based on the optimisation technique of swarm intelligence.Particle cluster algorithm utilizes bird
The foraging behavior of group is by the information sharing of group, to seek the optimal solution in global space.It imparts knowledge to students on one classroom of algorithm simulation
Interaction between student and the interaction and student of teacher mutually adjusts the optimal solution for seeking small range space between individual.
Invention content
It is an object of the invention to overcome the shortcomings of existing intelligent algorithm, a kind of combining with teaching algorithm and particle are proposed
The hybrid algorithm of group's algorithm can be searched while retaining particle cluster algorithm ability of searching optimum using the part of teaching algorithm
Suo Nengli improves the algorithm of convergence rate, to improve the efficiency and accuracy of power distribution network reconfiguration.
To achieve the above object, the technical solution adopted by the present invention is that：
Active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm, which is characterized in that the method
Including：
Step 1：The initial parameter of teaching algorithm and particle cluster algorithm, random initializtion population are set；
Step 2：Each particle is used and is searched for based on depth-first tree, topological analysis is carried out；
Step 3：The target function value for meeting irradiation structure constraint individual is calculated using object function, as fitness letter
Number；
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Iteration is updated to population using particle cluster algorithm and teaching algorithm, checks network topology, and calculate
The object function of individual；
Step 6：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 7：Step 5-6 is repeated until reaching maximum iteration；
Step 8：Export result.
Specifically, in step 3, to minimize network loss as object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbFor opening for branch b
Off status is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
In steps of 5, the iterative formula of particle cluster algorithm is：
In formula,It is the speed of particle；It is the position of particle；It is individual optimal value array；It is global optimum
Value；Nsw is the number of individuals of each iteration；c1And c2For accelerated factor；ρ is coefficient；r1And r2Respectively in (0,1) section with
Machine number.
In steps of 5, the teaching algorithm is divided into teaching phase and study stage；Population is carried out more using teaching algorithm
Newly the process of iteration is：
Teaching phase
In teaching phase, iteration, selects the individual of a target function value minimum as " old in group each time
Teacher ", other individuals are close to its, and iterative step is as follows：
TF=round [1+ri]
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold, XnewNew generated for teaching phase
Body； XoldFor original individual
In formula, TFIt is a factor, can use 1 or 2；For preferably individual；riFor for the random number in (0,1) section；Mi
For the mean value of individual；
The study stage
In the study stage, individual improves itself by learning from each other, and iterative step is as follows：
For i=1:nsw
Randomly choose XjAnd i ≠ j
If f (Xi)<f(Xj)
XNew, i=Xold,i+ri(Xi-Xj)
Otherwise
XNew, i=Xold,i+ri(Xj–Xi)
end
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
In formula, riRandom number between being 0 to 1, XnewThe new individual generated for the study stage；XoldFor original individual.
Compared with prior art, the present invention advantage is：
This method needle is in connection by teaching algorithm and particle cluster algorithm, is retaining particle cluster algorithm global optimizing ability
Meanwhile the local optimal searching ability and speed of algorithm are enhanced, to realize algorithm in global and local optimizing ability.The present invention
Network structure is updated by optimization algorithm iteration, plays the role of reducing network loss, to improve the efficiency of power distribution network reconfiguration and accurate
Property.
Description of the drawings
Fig. 1 is the active distribution network Intelligent Hybrid reconstruct side provided in an embodiment of the present invention based on teaching and particle cluster algorithm
The flow chart of method.
Specific implementation mode
Present disclosure is described in further details with reference to the accompanying drawings and detailed description.
Embodiment：
As shown in fig.1, for the active distribution network intelligence provided in an embodiment of the present invention based on teaching and particle cluster algorithm
The flow chart of reconstructing method is mixed, this method specifically includes following steps：
Step 1：The initial parameter of teaching algorithm and particle cluster algorithm, random initializtion population are set；
Step 2：Each particle is used and is searched for based on depth-first tree, topological analysis is carried out；Wherein, which is
Hope's Crow Fu Te raises " Depth-First-Search " of proposition with Robert's tower；
Step 3：The target function value that each individual is calculated using object function, as fitness function value；
To minimize network loss as object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbFor opening for branch b
Off status is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
Certainly, object function will meet maximum current constraint, maximum voltage constraint, trend constraint and radiativity network simultaneously
The constraint of structure.
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Individual is updated using particle cluster algorithm, checks network topology, and calculates the object function of individual；
The step of particle cluster algorithm, is as follows：
In formula,It is the speed of particle；It is the position of particle；It is individual optimal value array；Be it is global most
The figure of merit；Nsw is the number of individuals of each iteration, c1And c2For accelerated factor；In particle cluster algorithm, the position of particleAnd speed
DegreeIt is a particle coding；ρ is coefficient, generally 1；r1And r2Random number respectively in (0,1) section.
Step 6：Using teaching algorithm individual is updated, check network topology, and calculate individual object function and
Fitness function；
In algorithm of imparting knowledge to students, it is divided into teaching phase and study stage.Teaching algorithm iteration formula and iterative step are：
Teaching phase
In teaching phase, iteration, selects best (target function value is a minimum) individual to make in group each time
For " teacher ", other individuals are close to its, and iterative step is as follows：
TF=round [1+ri]
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold。
In formula, TFIt is a factor, can use 1 or 2.
The study stage
In the study stage, individual improves itself by learning from each other, and iterative step is as follows：
For i=1:nsw
Randomly choose XjAnd i ≠ j
If f (Xi)<f(Xj)
XNew, i=Xold,i+ri(Xi-Xj)
Otherwise
XNew, i=Xold,i+ri(Xj–Xi)
end
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold。
In formula, riRandom number between being 0 to 1.
Wherein, it should be noted that the iteration sequence of two kinds of algorithms of step 5 and step 6 is commutative, that is to say, that step 5
There is no sequencings between the two with step 6.
Step 7：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 8：Step 5-7 is repeated until reaching maximum iteration；
Step 9：Export result.
It follows that this method needle will impart knowledge to students, algorithm and particle cluster algorithm are in connection, global retaining particle cluster algorithm
While optimizing ability, the local optimal searching ability and speed of algorithm are enhanced, to realize algorithm in global and local optimizing energy
Power.The present invention updates network structure by optimization algorithm iteration, plays the role of reducing network loss.
Above-described embodiment simply to illustrate that the present invention technical concepts and features, it is in the art the purpose is to be to allow
Those of ordinary skill cans understand the content of the present invention and implement it accordingly, and it is not intended to limit the scope of the present invention.It is all
It is the equivalent changes or modifications made according to the essence of the content of present invention, should all covers within the scope of the present invention.
Claims (5)
1. the active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm, which is characterized in that the method packet
It includes：
Step 1：The initial parameter of teaching algorithm and particle cluster algorithm, random initializtion population are set；
Step 2：Each particle is used and is searched for based on depth-first tree, topological analysis is carried out；
Step 3：The target function value for meeting irradiation structure constraint individual is calculated using object function, as fitness function；
Step 4：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 5：Iteration is updated to population using particle cluster algorithm and teaching algorithm, checks network topology, and calculate individual
Object function；
Step 6：The optimal solution P of the corresponding individual of updateiWith globally optimal solution Pg；
Step 7：Step 5-6 is repeated until reaching maximum iteration；
Step 8：Export result.
2. the active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm as described in claim 1, feature
It is,
In step 3, to minimize network loss as object function：
Wherein, NbranchFor the quantity of network edge；IbFor the electric current of b branches；RbFor the resistance of b branches；kbFor the switch shape of branch b
State is worth and indicates that switch is closed for 1, is worth and indicates that switch disconnects for 0.
3. the active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm as described in claim 1, feature
It is, in steps of 5, the iterative formula of particle cluster algorithm is：
In formula, Veli kIt is the speed of particle；Xi kIt is the position of particle；Pi kIt is individual optimal value array；Pg kIt is global optimum；
Nsw is the number of individuals of each iteration；c1And c2For accelerated factor；ρ is coefficient；r1And r2It is respectively random in (0,1) section
Number.
4. the active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm as described in claim 1, feature
It is, in steps of 5, the teaching algorithm is divided into teaching phase and study stage；Population is updated using teaching algorithm
The process of iteration is：
Teaching phase
In teaching phase, iteration each time, the individual for selecting a target function value minimum in group as " teacher ",
His individual is close to its, and iterative step is as follows：
TF=round [1+ri]
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold, XnewThe new individual generated for teaching phase；Xold
For original individual
In formula, TFIt is a factor, can use 1 or 2；For preferably individual；riFor for the random number in (0,1) section；MiIt is a
The mean value of body；
The study stage
In the study stage, individual improves itself by learning from each other, and iterative step is as follows：
For i=1:nsw
Randomly choose XjAnd i ≠ j
If f (Xi)<f(Xj)
XNew, i=Xold,i+ri(Xi-Xj)
Otherwise
XNew, i=Xold,i+ri(Xj–Xi)
end
If f (Xnew)<f(Xold), use XnewInstead of Xold, no side Xnew=Xold；
In formula, riRandom number between being 0 to 1, XnewThe new individual generated for the study stage；XoldFor original individual.
5. the active distribution network Intelligent Hybrid reconstructing method based on teaching and particle cluster algorithm as claimed in claim 2, feature
Be, the object function to meet maximum current constraint, maximum voltage constraint, trend constraint and radiativity network structure pact
Beam.
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| CN109754128A (en) * | 2019-02-18 | 2019-05-14 | 东北电力大学 | A kind of wind/light/storage/bavin micro-capacitance sensor Optimal Configuration Method of meter and meteorological wave characteristic difference typical scene |
| CN111598294A (en) * | 2020-04-13 | 2020-08-28 | 国网江西省电力有限公司电力科学研究院 | Active power distribution network reconstruction algorithm and device based on improved teaching optimization |
| CN111932012A (en) * | 2020-08-12 | 2020-11-13 | 国网黑龙江省电力有限公司哈尔滨供电公司 | Energy storage system-distributed power supply-capacitor comprehensive control reactive power optimization method |
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| CN109754128A (en) * | 2019-02-18 | 2019-05-14 | 东北电力大学 | A kind of wind/light/storage/bavin micro-capacitance sensor Optimal Configuration Method of meter and meteorological wave characteristic difference typical scene |
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| CN111932012B (en) * | 2020-08-12 | 2023-07-28 | 国网黑龙江省电力有限公司哈尔滨供电公司 | Energy storage system-distributed power supply-capacitor integrated control reactive power optimization method |
| CN112803404A (en) * | 2021-02-25 | 2021-05-14 | 国网河北省电力有限公司经济技术研究院 | Self-healing reconstruction planning method and device for power distribution network and terminal |
| CN112803404B (en) * | 2021-02-25 | 2023-03-14 | 国网河北省电力有限公司经济技术研究院 | Self-healing reconstruction planning method and device for power distribution network and terminal |
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